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Lookup NU author(s): Professor Charles Harvey,
Professor Mairi Maclean
This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY-NC-ND).
Longitudinal studies with a mix of binary outcomes and continuous variables are common in organizational research. Selecting the dependent variable is often difficult due to conflicting theories and contradictory empirical studies. In addition, organizational researchers are confronted with methodological challenges posed by latent variables relating to observed binary outcomes and within-subject correlation. We draw on Dueker’s (2005) qualitative vector autoregression (QVAR) and Lunn et al.’s (2014) multivariate probit model to develop a solution to these problems in the form of a qualitative short panel vector autoregression (QSP-VAR). The QSP-VAR combines binary and continuous variables into a single vector of dependent variables, making every variable endogenous a priori. The QSP-VAR identifies causal order, reveals within-subject correlation and accounts for latent variables. Using a Bayesian approach, the QSP-VAR provides reliable inference for short time dimension longitudinal research. This is demonstrated through analysis of the durability of elite corporate agents, social networks and firm performance in France. We provide our OpenBUGS code to enable implementation of the QSP-VAR by other researchers.
Author(s): Kling G, Harvey C, Maclean M
Publication type: Article
Publication status: Published
Journal: Organizational Research Methods
Print publication date: 01/10/2017
Online publication date: 30/11/2015
Acceptance date: 22/10/2015
Date deposited: 15/10/2015
ISSN (print): 1094-4281
ISSN (electronic): 1552-7425
Publisher: Sage Publications Ltd.
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